RONC-03. NEUROCOGNITIVE CHANGES AFTER RADIATION FOR PEDIATRIC BRAIN TUMOURS: WHICH BRAIN SUBSTRUCTURES ARE MOST IMPORTANT?
Bibliographic record
Abstract
Abstract INTRODUCTION The contribution of different intracranial structures on neurocognitive decline after radiation therapy (RT) in children is unclear. METHODS This was a retrospective study of children with brain tumours treated from 2005 to 2017. Patients with longitudinal neurocognitive assessments and photon dosimetric data (if RT given) were included. Full scale intelligence quotient (FSIQ) was the primary endpoint; sub-indices of neurocognition were modelled separately (perceptual reasoning [PRI], processing speed [PSI], verbal comprehension [VCI] and working memory [WMI]). Multivariable linear mixed effects models were used to model endpoints, with age at diagnosis & dose to different brain regions as fixed effects and patient-specific random intercepts. RESULTS Sixty-nine patients were included; ten patients did not receive any RT (i.e. low-grade glioma). Median neurocognitive follow-up was 3.2 years. Right hippocampus mean dose was a strong predictor of declines in FSIQ (p < .001), VCI (p = 0.002) and PRI (p = 0.049). Dose to 50% of the supratentorial brain (D50) was the strongest predictor for WMI (p < .001) and PSI (p < .001). Each gray increase in mean right hippocampus dose resulted in a decrease of 0.038 FSIQ points/year. After adjusting for dose to brain substructures, younger age & presence of a ventriculoperitoneal shunt were also associated with decreased FSIQ. CONCLUSIONS Mean dose to the right hippocampus was associated with declines in FSIQ, VCI and PRI, while supratentorial brain D50 was associated with WMI and PSI. Efforts should be made to reduce unnecessary dose to these brain structures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".